






'''simple docstring'''







from __future__ import annotations


def       __UpperCamelCase					(      lowercase_       :  str   ,       lowercase_       :  list[str] | None = None   ,       lowercase_       :  dict[str, float] | None = None   ,       lowercase_       :  bool = False   ,       ):
       """simple docstring"""




       a_						     =			cipher_alphabet or [chr(lowercase_   ) for i in range(97   ,       123   )]

       # If the argument is None or the user provided an empty dictionary
       if not frequencies_dict:
              # Frequencies of letters in the english language (how much they show up)
              a_						     =			{
                  'a': 0.0_8497,
                  'b': 0.0_1492,
                  'c': 0.0_2202,
                  'd': 0.0_4253,
                  'e': 0.1_1162,
                  'f': 0.0_2228,
                  'g': 0.0_2015,
                  'h': 0.0_6094,
                  'i': 0.0_7546,
                  'j': 0.0_0153,
                  'k': 0.0_1292,
                  'l': 0.0_4025,
                  'm': 0.0_2406,
                  'n': 0.0_6749,
                  'o': 0.0_7507,
                  'p': 0.0_1929,
                  'q': 0.0_0095,
                  'r': 0.0_7587,
                  's': 0.0_6327,
                  't': 0.0_9356,
                  'u': 0.0_2758,
                  'v': 0.0_0978,
                  'w': 0.0_2560,
                  'x': 0.0_0150,
                  'y': 0.0_1994,
                  'z': 0.0_0077,
              }
       else:
              # Custom frequencies dictionary
              a_						     =			frequencies_dict

       if not case_sensitive:
              a_						     =			ciphertext.lower()

       # Chi squared statistic values
       a_						     =			{}

       # cycle through all of the shifts
       for shift in range(len(lowercase_   )   ):
              a_						     =			''

              # decrypt the message with the shift
              for letter in ciphertext:
                     try:
                            # Try to index the letter in the alphabet
                            a_						     =			(alphabet_letters.index(letter.lower()   ) - shift) % len(
                                lowercase_   )
                            decrypted_with_shift += (
                                alphabet_letters[new_key].upper()
                                if case_sensitive and letter.isupper()
                                else alphabet_letters[new_key]
                            )
                     except ValueError:
                            # Append the character if it isn't in the alphabet
                            decrypted_with_shift += letter

              a_						     =			0.0

              # Loop through each letter in the decoded message with the shift
              for letter in decrypted_with_shift:
                     if case_sensitive:
                            a_						     =			letter.lower()
                            if letter in frequencies:
                                   # Get the amount of times the letter occurs in the message
                                   a_						     =			decrypted_with_shift.lower().count(lowercase_   )

                                   # Get the excepcted amount of times the letter should appear based
                                   # on letter frequencies
                                   a_						     =			frequencies[letter] * occurrences

                                   # Complete the chi squared statistic formula
                                   a_						     =			((occurrences - expected) ** 2) / expected

                                   # Add the margin of error to the total chi squared statistic
                                   chi_squared_statistic += chi_letter_value
                     else:
                            if letter.lower() in frequencies:
                                   # Get the amount of times the letter occurs in the message
                                   a_						     =			decrypted_with_shift.count(lowercase_   )

                                   # Get the excepcted amount of times the letter should appear based
                                   # on letter frequencies
                                   a_						     =			frequencies[letter] * occurrences

                                   # Complete the chi squared statistic formula
                                   a_						     =			((occurrences - expected) ** 2) / expected

                                   # Add the margin of error to the total chi squared statistic
                                   chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
              a_						     =			(
                  chi_squared_statistic,
                  decrypted_with_shift,
              )

       # Get the most likely cipher by finding the cipher with the smallest chi squared
       # statistic
       def chi_squared_statistic_values_sorting_key(lowercase_       :  int   ) -> tuple[float, str]:
              return chi_squared_statistic_values[key]

       a_						     =			min(
           lowercase_   ,       key=lowercase_   ,       )

       # Get all the data from the most likely cipher (key, decoded message)
       (
           (
           a_
       )     ,	(
           a_
       )     ,	
       )						     =			chi_squared_statistic_values[most_likely_cipher]

       # Return the data on the most likely shift
       return (
           most_likely_cipher,
           most_likely_cipher_chi_squared_value,
           decoded_most_likely_cipher,
       )












'''simple docstring'''







import os
import sys
import unittest


__lowerCAmelCase        =							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


__lowerCAmelCase        =							os.path.join("tests", "models", "bert", "test_modeling_bert.py")
__lowerCAmelCase        =							os.path.join("tests", "models", "blip", "test_modeling_blip.py")




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_test_to_tester_mapping(UpperCamelCase__	)
         a_						     =			get_test_to_tester_mapping(UpperCamelCase__	)

         a_						     =			{'BertModelTest': 'BertModelTester'}

         a_						     =			{
             'BlipModelTest': 'BlipModelTester',
             'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
             'BlipTextModelTest': 'BlipTextModelTester',
             'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
             'BlipVQAModelTest': 'BlipVQAModelTester',
             'BlipVisionModelTest': 'BlipVisionModelTester',
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_model_to_test_mapping(UpperCamelCase__	)
         a_						     =			get_model_to_test_mapping(UpperCamelCase__	)

         a_						     =			{
             'BertForMaskedLM': ['BertModelTest'],
             'BertForMultipleChoice': ['BertModelTest'],
             'BertForNextSentencePrediction': ['BertModelTest'],
             'BertForPreTraining': ['BertModelTest'],
             'BertForQuestionAnswering': ['BertModelTest'],
             'BertForSequenceClassification': ['BertModelTest'],
             'BertForTokenClassification': ['BertModelTest'],
             'BertLMHeadModel': ['BertModelTest'],
             'BertModel': ['BertModelTest'],
         }

         a_						     =			{
             'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
             'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
             'BlipForQuestionAnswering': ['BlipVQAModelTest'],
             'BlipModel': ['BlipModelTest'],
             'BlipTextModel': ['BlipTextModelTest'],
             'BlipVisionModel': ['BlipVisionModelTest'],
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_model_to_tester_mapping(UpperCamelCase__	)
         a_						     =			get_model_to_tester_mapping(UpperCamelCase__	)

         a_						     =			{
             'BertForMaskedLM': ['BertModelTester'],
             'BertForMultipleChoice': ['BertModelTester'],
             'BertForNextSentencePrediction': ['BertModelTester'],
             'BertForPreTraining': ['BertModelTester'],
             'BertForQuestionAnswering': ['BertModelTester'],
             'BertForSequenceClassification': ['BertModelTester'],
             'BertForTokenClassification': ['BertModelTester'],
             'BertLMHeadModel': ['BertModelTester'],
             'BertModel': ['BertModelTester'],
         }

         a_						     =			{
             'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
             'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
             'BlipForQuestionAnswering': ['BlipVQAModelTester'],
             'BlipModel': ['BlipModelTester'],
             'BlipTextModel': ['BlipTextModelTester'],
             'BlipVisionModel': ['BlipVisionModelTester'],
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)




